Transcript Testing Biological Ideas on Evolution, Ageing and

Testing Biological Ideas on
Evolution, Ageing and Longevity
with Demographic and
Genealogical Data
Leonid A. Gavrilov
Natalia S. Gavrilova
Center on Aging, NORC/University of Chicago,
1155 East 60th Street, Chicago, IL 60637
Is There Any Link Between
Longevity and Fertility?
What are the data and the predictions of
the evolutionary theory on this issue?
Brief Historical Note
• Beeton, M., Yule, G.U., Pearson, K. 1900. Data
for the problem of evolution in man. V. On the
correlation between duration of life and the
number of offspring. Proc. R. Soc. London, 67:
159-179.
• Data used: English Quaker records and Whitney
Family of Connectucut records for females and
American Whitney family and Burke’s ‘Landed
Gentry’ for males.
Findings and Conclusions
by Beeton et al., 1900
• They tested predictions of the Darwinian
evolutionary theory that the fittest individuals
should leave more offspring.
• Findings: Slightly positive relationship between
postreproductive lifespan (50+) of both mothers
and fathers and the number of offspring.
• Conclusion: “fertility is correlated with longevity
even after the fecund period is passed” and
“selective mortality reduces the numbers of the
offspring of the less fit relatively to the fitter.”
Other Studies, Which Found Positive
Correlation Between Reproduction and
Postreproductive Longevity
• Alexander Graham Bell (1918): “The longer lived
parents were the most fertile.”
• Bettie Freeman (1935): Weak positive correlations
between the duration of postreproductive life in
women and the number of offspring borne. Human
Biology, 7: 392-418.
• Bideau A. (1986): Duration of life in women after
age 45 was longer for those women who borne 12
or more children. Population 41: 59-72.
Studies that Found no Relationship
Between Postreproductive Longevity and
Reproduction
• Henry L. 1956. Travaux et Documents.
• Gauter, E. and Henry L. 1958. Travaux et
Documents, 26.
• Knodel, J. 1988. Demographic Behavior in
the Past.
• Le Bourg et al., 1993. Experimental
Gerontology, 28: 217-232.
Study that Found a Trade-Off Between
Reproductive Success and Postreproductive
Longevity
• Westendorp RGJ, Kirkwood TBL. 1998. Human
longevity at the cost of reproductive success.
Nature 396: 743-746.
• Extensive media coverage including BBC and
over 70 citations in scientific literature as an
established scientific fact. Previous studies were
not quoted and discussed in this article.
Do longevous women have impaired fertility ?
Why is this question so important and interesting:
•
Scientific Significance. This is a testable prediction of some evolutionary theories of
aging (disposable soma theory of aging, Westendorp, Kirkwood, 1998)
•
Practical Importance. Do we really wish to live a long life at the cost of infertility?
Based these concerns a suggestion was made:
"... increasing longevity through genetic manipulation of the mechanisms of aging
raises deep biological and moral questions. These questions should give us pause before
we embark on the enterprise of extending our lives“
Walter Glennon "Extending the Human Life Span", Journal of Medicine and Philosophy,
2002, Vol. 27, No. 3, pp. 339-354
•
Educational Significance. Do we teach our students right? Impaired fertility of
longevous women is often presented in scientific literature and mass media as already
established fact (Kirkwood, 2002; Westendorp, 2002; Glennon, 2002; Perls et al.,
2002 etc.) Is it a fact or artifact ?
Point estimates of progeny number for married aristocratic women
from different birth cohorts as a function of age at death.
The estimates of progeny number are adjusted for trends over calendar time
using multiple regression.
Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human longevity
at the cost of reproductive success. Nature, 1998, 396, pp 743746
Number of progeny and age at first childbirth dependent on
the age at death of married aristocratic women
Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human
longevity at the cost of reproductive success. Nature,
1998, 396, pp 743-746
“… it is not a matter of reduced fertility, but
a case of 'to have or have not'.“
Table 1 Relationship between age at death and number of children for married aristocratic women
Age at death
Proportion childless
(years)
Number of children
mean for all women
mean for women having children
<20
0.66
0.45
1.32
21-30
0.39
1.35
2.21
31-40
0.26
2.05
2.77
41-50
0.31
2.01
2.91
51-60
0.28
2.4
3.33
61-70
0.33
2.36
3.52
71-80
0.31
2.64
3.83
81-90
0.45
2.08
3.78
>90
0.49
1.80
3.53
Source: Toon Ligtenberg & Henk Brand. Longevity — does family
size matter? Nature, 1998, 396, pp 743-746
Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human
longevity at the cost of reproductive success. Nature,
1998, 396, pp 743-746
General Methodological Principle:
• Before making strong conclusions, consider all other
possible explanations, including potential flaws in
data quality and analysis
• Previous analysis by Westendorp and Kirkwood was
made on the assumption of data completeness:
Number of children born = Number of children recorded
• Potential concerns: data incompleteness, under-reporting
of short-lived children, women (because of patrilineal
structure of genealogical records), persons who did not
marry or did not have children.
Number of children born >> Number of children recorded
Test for Data Completeness
Direct Test: Cross-checking of the initial dataset with other data sources
We examined 335 claims of childlessness in the dataset used by Westendorp and
Kirkwood. When we cross-checked these claims with other professional sources
of data, we found that at least 107 allegedly childless women (32%) did have
children!
At least 32% of childlessness claims proved to be wrong ("false negative claims") !
Some illustrative examples:
Henrietta Kerr (16531741) was apparently childless in the dataset used by Westendorp and Kirkwood and lived 88
years. Our cross-checking revealed that she did have at least one child, Sir William Scott (2nd Baronet of
Thirlstane, died on October 8, 1725).
Charlotte Primrose (17761864) was also considered childless in the initial dataset and lived 88 years. Our crosschecking of the data revealed that in fact she had as many as five children: Charlotte (18031886), Henry (18061889), Charles (18071882), Arabella (1809-1884), and William (18151881).
Wilhelmina Louise von Anhalt-Bernburg (17991882), apparently childless, lived 83 years. In reality, however, she
had at least two children, Alexander (18201896) and Georg (18261902).
Point estimates of progeny number for married aristocratic women from different birth
cohorts as a function of age at death.
The estimates of progeny number are adjusted for trends over calendar time using multiple
regression.
Source: Westendorp, R. G. J., Kirkwood, T. B. L. Human longevity at the cost of
reproductive success. Nature, 1998, 396, pp 743-746
Antoinette de Bourbon
(1493-1583)
Lived almost 90 years
She was claimed to have only one child in the
dataset used by Westendorp and Kirkwood:
Marie (1515-1560), who became a mother of
famous Queen of Scotland, Mary Stuart.
Our data cross-checking revealed that in fact
Antoinette had 12 children!
•
•
•
•
•
•
•
•
•
•
•
•
Marie 1515-1560
Francois Ier 1519-1563
Louise 1521-1542
Renee 1522-1602
Charles 1524-1574
Claude 1526-1573
Louis 1527-1579
Philippe 1529-1529
Pierre 1529
Antoinette 1531-1561
Francois 1534-1563
Rene 1536-1566
Testing Evolutionary Theories of
Ageing and Mutation Accumulation
Theory in Particular
• Mutation accumulation theory predicts that those
deleterious mutations that are expressed in later
life should have higher frequencies (because
mutation-selection balance is shifted to higher
equilibrium frequencies due to smaller selection
pressure).
• Therefore, ‘expressed’ genetic variability should
increase with age.
• This should result in higher heritability estimates
for lifespan of offspring born to longer-lived
parents.
Characteristics of Our Data Sample
for ‘Reproduction-Longevity’ Studies
• 3,723 married women
born in 1500-1875 and
belonging to the upper
European nobility.
• Women with two or more
marriages (5%) were
excluded from the analysis
in order to facilitate the
interpretation of results
(continuity of exposure to
childbearing).
•Every case of
childlessness has been
checked using at least two
different genealogical
sources.
Proportion of Childless Women
as a Function of Their Lifespan
Univariate data for
3,723 European aristocratic women born in 1500-1875
50
Compare these results with the
Knodel's (1988) estimates for German villages
in the 18th and 19th centuries: 6.7-16.2%
Percent of Childlessness
40
30
20
10
0
<20
20-29 30-39 40-49 50-59 60-69 70-79 80-89
Women's Lifespan
90+
Childlessness Odds Ratio Estimates
as a Function of Wife's Lifespan
Multivariate logistic regression analysis of
3,723 European aristocratic families
Childlessness Odds Ratio (Net Effect)
10
Net effects, adjusted for calendar year of birth,
maternal age at marriage, husband's lifespan
and husband's age at marriage
8
37
6
4
2
294
572
359
483
123
628
872
355
0
<20
20-29 30-39 40-49 50-59 60-69 70-79 80-89
Wife's Lifespan
90+
Childlessness Odds Ratio Estimates
as a Function of Husband's Lifespan
Multivariate logistic regression analysis of
3,723 European aristocratic families
Childlessness Odds Ratio (Net Effect)
5
Net effects, adjusted for calendar year of birth,
wife's age at marriage, wife's lifespan
and husband's age at marriage
4
61
3
2
1
51
0
<30
30-39 40-49 50-59 60-69 70-79 80-89
Husband's Lifespan
90+
Characteristic of our Dataset
• Over 16,000 persons
belonging to the European
aristocracy
• 1800-1880 extinct birth
cohorts
• Adult persons aged 30+
• Data extracted from the
professional genealogical
data sources including
Genealogisches Handbook
des Adels, Almanac de
Gotha, Burke Peerage and
Baronetage.
Daughter's Lifespan
(Mean Deviation from Cohort Life Expectancy)
Daughter's Lifespan (deviation), years
as a Function of Paternal Lifespan
6
4
2
0
-2
40
50
60
70
80
90
Paternal Lifespan, years
100
• Offspring data
for adult lifespan
(30+ years) are
smoothed by
5-year running
average.
• Extinct birth
cohorts (born in
1800-1880)
• European
aristocratic
families.
6,443 cases
Offspring Lifespan at Age 30
as a Function of Paternal Lifespan
Data are adjusted for other predictor variables
4
2
p=0.006
p=0.05
0
p=0.001
4
Lifespan difference, years
Lifespan difference, years
p=0.0003
p<0.0001
p=0.001
2
0
-2
-2
40
50
60
70
80
90
Paternal Lifespan, years
Daughters, 8,284 cases
100
40
50
60
70
80
90
Paternal Lifespan, years
Sons, 8,322 cases
100
Offspring Lifespan at Age 60
as a Function of Paternal Lifespan
Data are adjusted for other predictor variables
4
p=0.0001
2
p=0.04
p=0.04
0
Lifespan difference, years
Lifespan difference, years
4
p=0.0003
2
p=0.004
p=0.006
0
-2
-2
40
50
60
70
80
90
Paternal Lifespan, years
Daughters, 6,517 cases
100
40
50
60
70
80
90
Paternal Lifespan, years
Sons, 5,419 cases
100
Offspring Lifespan at Age 30
as a Function of Maternal Lifespan
Data are adjusted for other predictor variables
4
p=0.0004
p=0.02
Lifespan difference, years
Lifespan difference, years
4
2
p=0.01
p=0.05
0
2
0
-2
-2
40
50
60
70
80
90
100
Maternal Lifespan, years
Daughters, 8,284 cases
40
50
60
70
80
90
Maternal Lifespan, years
Sons, 8,322 cases
100
Offspring Lifespan at Age 60
as a Function of Maternal Lifespan
Data are adjusted for other predictor variables
4
Lifespan difference, years
Lifespan difference, years
p<0.0001
4
2
p=0.01
p=0.01
0
p=0.04
2
0
-2
-2
40
50
60
70
80
90
100
Maternal Lifespan, years
Daughters, 6,517 cases
40
50
60
70
80
90
Maternal Lifespan, years
Sons, 5,419 cases
100
Person’s Lifespan as a Function
of Spouse Lifespan
Data are adjusted for other predictor variables
6
Lifespan difference, years
Lifespan difference, years
6
4
2
0
4
2
0
-2
-2
40
50
60
70
80
90
100
Spouse Lifespan, years
Married Women, 6,442 cases
-4
40
50
60
70
80
90
100
Spouse Lifespan, years
Married Men, 6,596 cases
Daughters' Lifespan (30+) as a Function
of Paternal Age at Daughter's Birth
6,032 daughters from European aristocratic families
born in 1800-1880
1
•
Life expectancy of adult women
(30+) as a function of father's
age when these women were
born (expressed as a difference
from the reference level for
those born to fathers of 40-44
years).
•
The data are point estimates
(with standard errors) of the
differential intercept coefficients
adjusted for other explanatory
variables using multiple
regression with nominal
variables.
•
Daughters of parents who
survived to 50 years.
Lifespan Difference (yr)
0
-1
-2
-3
p = 0.04
-4
15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59
Paternal Age at Reproduction
Daughters' Lifespan (60+) as a Function
of Paternal Age at Daughter's Birth
4,832 daughters from European aristocratic families
born in 1800-1880
1
•
Life expectancy of older
women (60+) as a function of
father's age when these women
were born (expressed as a
difference from the reference
level for those born to fathers of
40-44 years).
•
The data are point estimates
(with standard errors) of the
differential intercept coefficients
adjusted for other explanatory
variables using multiple
regression with nominal
variables.
•
Daughters of parents who
survived to 50 years.
Lifespan Difference (yr)
0
-1
-2
p = 0.004
-3
15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59
Paternal Age at Reproduction
Paternal Age as a Risk Factor
for Alzheimer Disease
Parental age at childbirth (years)
40
• MGAD - major
gene for Alzheimer
Disease
p = 0.04
35
NS
p=0.04
NS
NS
30
NS
25
Paternal age
Maternal age
Sporadic Alzheimer Disease (low likelihood of MGAD)
Familial Alzheimer Disease (high likelihood of MGAD)
Controls
• Source: L. Bertram
et al.
Neurogenetics,
1998, 1: 277-280.
Paternal Age and Risk
of Schizophrenia
•
Estimated cumulative
incidence and
percentage of offspring
estimated to have an
onset of schizophrenia
by age 34 years, for
categories of paternal
age. The numbers
above the bars show
the proportion of
offspring who were
estimated to have an
onset of schizophrenia
by 34 years of age.
•
Source: Malaspina et al.,
Arch Gen
Psychiatry.2001.
Molecular Effects on Ageing
New Ideas and Findings by Bruce Ames:
• The rate of mutation damage is NOT immutable, but it can be
dramatically decreased by very simple measures:
-- Through elimination of deficiencies in vitamins and other
micronutrients (iron, zinc, magnesium, etc).
• Micronutrient deficiencies are very common even in the modern
wealthy populations
• These deficiencies are much more important than radiation, industrial
pollution and most other hazards
Our hypothesis:
Remarkable improvement in the oldest-old survival may reflect an unintended
retardation of the aging process, caused by decreased damage accumulation,
because of improving the micronutrient status in recent decades
Micronutrient Undernutrition in Americans
Nutrient
% ingesting
Population Group
RDA
< RDA
% ingesting
<<50%
50% RDA
RDA
Minerals
Iron
Women 20-30 years
18 mg
75%
25%
Women 50+ years
8 mg
25%
5-10%
Men; Women 50+ years
11; 8 mg
50%
10%
B6
Men; Women
1.7; 1.5 mg
50%
10%
Folate**
Men; Women
400 mcg
75%
25%; 50%
B12
Men; Women
2.4 mcg
10-20; 25-50 %
5; ~10-25%
C
Men; Women
90; 75 mg
50%
25%
Zinc
Vitamins
•Wakimoto and Block (2001) J Gerontol A Biol Sci Med Sci. Oct; 56 Spec No 2(2):65-80.
** Before U.S. Food Fortification
Source: Presentation by Bruce Ames at the IABG Congress
Molecular Effects on Ageing (2)
Ideas and Findings by Bruce Ames:
• The rate of damage accumulation is NOT immutable, but it can be
dramatically decreased by PREVENTING INFLAMMATION:
Inflammation causes tissue damage through many mechanisms
including production of Hypochlorous acid (HOCl), which produces
DNA damage (through incorporation of chlorinated nucleosides).
Chronic inflammation may contribute to many age-related degenerative
diseases including cancer
Hypothesis:
Remarkable improvement in the oldest-old survival may reflect an unintended
retardation of the aging process, caused by decreased damage accumulation,
because of partial PREVENTION of INFLAMMATION through better control
over infectious diseases in recent decades
Season of Birth and Female Lifespan
8,284 females from European aristocratic families
born in 1800-1880
Seasonal Differences in Adult Lifespan at Age 30
3
•
Life expectancy of adult
women (30+) as a function of
month of birth (expressed as
a difference from the
reference level for those
born in February).
•
The data are point estimates
(with standard errors) of the
differential intercept
coefficients adjusted for
other explanatory variables
using multivariate
regression with categorized
nominal variables.
p=0.006
Lifespan Difference (yr)
p=0.02
2
1
0
FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB.
Month of Birth
Season of Birth and Female Lifespan
6,517 females from European aristocratic families
born in 1800-1880
Seasonal Differences in Adult Lifespan at Age 60
2
•
Life expectancy of adult
women (60+) as a function of
month of birth (expressed as
a difference from the
reference level for those
born in February).
•
The data are point estimates
(with standard errors) of the
differential intercept
coefficients adjusted for
other explanatory variables
using multivariate
regression with categorized
nominal variables.
Lifespan Difference (yr)
p=0.008
p=0.04
1
0
FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB.
Month of Birth
Mean Lifespan of Females
Born in December and February
as a Function of Birth Year
Mean Lifespan, years
80
75
70
65
60
Born in February
Born in December
Linear Regression Fit
1800
1820
1840
1860
Year of Birth
1880
• Life
expectancy of
adult women
(30+) as a
function of
year of birth
Aging is a Very General Phenomenon!
What Should
the Aging Theory Explain:
• Why do most biological species deteriorate with age?
• Specifically, why do mortality rates increase exponentially
with age in many adult species (Gompertz law)?
• Why does the age-related increase in mortality rates vanish
at older ages (mortality deceleration)?
• How do we explain the so-called compensation law of
mortality (Gavrilov & Gavrilova, 1991)?
Exponential Increase of Death Rate
with Age in Fruit Flies
(Gompertz Law of Mortality)
Linear dependence of
the logarithm of
mortality force on the
age of Drosophila.
Based on the life table
for 2400 females of
Drosophila melanogaster
published by Hall (1969).
Mortality force was
calculated for 3-day age
intervals.
Source: Gavrilov, Gavrilova,
“The Biology of Life Span” 1991
Age-Trajectory of Mortality in Flour Beetles
(Gompertz-Makeham Law of Mortality)
Dependence of the
logarithm of mortality force
(1) and logarithm of
increment of mortality force
(2) on the age of flour
beetles (Tribolium confusum
Duval).
Based on the life table
for 400 female flour beetles
published by Pearl and
Miner (1941). Mortality
force was calculated for 30day age intervals.
Source: Gavrilov, Gavrilova,
“The Biology of Life Span”
1991
Age-Trajectory of Mortality in Italian Women
(Gompertz-Makeham Law of Mortality)
Dependence of the
logarithm of mortality
force (1) and logarithm of
increment of mortality
force (2) on the age of
Italian women.
Based on the official
Italian period life table for
1964-1967. Mortality force
was calculated for 1-year
age intervals.
Source: Gavrilov, Gavrilova,
“The Biology of Life Span”
1991
Compensation Law of Mortality
Convergence of Mortality Rates with Age
1 – India, 1941-1950, males
2 – Turkey, 1950-1951, males
3 – Kenya, 1969, males
4 - Northern Ireland, 1950-1952,
males
5 - England and Wales, 19301932, females
6 - Austria, 1959-1961, females
7 - Norway, 1956-1960, females
Source: Gavrilov, Gavrilova,
“The Biology of Life Span” 1991
Compensation Law of Mortality
in Laboratory Drosophila
1 – drosophila of the Old Falmouth,
New Falmouth, Sepia and Eagle
Point strains (1,000 virgin
females)
2 – drosophila of the Canton-S strain
(1,200 males)
3 – drosophila of the Canton-S strain
(1,200 females)
4 - drosophila of the Canton-S strain
(2,400 virgin females)
Mortality force was calculated for 6day age intervals.
Source: Gavrilov, Gavrilova,
“The Biology of Life Span” 1991
Mortality at Advanced Ages
Source: Gavrilov L.A., Gavrilova N.S. The Biology of Life Span:
A Quantitative Approach, NY: Harwood Academic Publisher, 1991
M. Greenwood, J. O. Irwin. BIOSTATISTICS OF SENILITY
Survival Patterns After Age 90
Percent surviving (in log scale) is
plotted as a function of age of Swedish
women for calendar years 1900, 1980,
and 1999 (cross-sectional data). Note
that after age 100, the logarithm of
survival fraction is decreasing without
much further acceleration (aging) in
almost a linear fashion. Also note an
increasing pace of survival improvement
in history: it took less than 20 years
(from year 1980 to year 1999) to repeat
essentially the same survival
improvement that initially took 80 years
(from year 1900 to year 1980).
Source: cross-sectional (period) life
tables at the Berkeley Mortality
Database (BMD):
http://www.demog.berkeley.edu/~bmd/
Non-Gompertzian Mortality Kinetics
of Four Invertebrate Species
Non-Gompertzian mortality
kinetics of four invertebrate
species: nematodes,
Campanularia flexuosa,
rotifers and shrimp.
Source: A. Economos.
A non-Gompertzian
paradigm for mortality
kinetics of metazoan
animals and failure kinetics
of manufactured products.
AGE, 1979, 2: 74-76.
Non-Gompertzian Mortality Kinetics
of Three Rodent Species
Non-Gompertzian
mortality kinetics of
three rodent species:
guinea pigs, rats and
mice.
Source: A. Economos.
A non-Gompertzian
paradigm for mortality
kinetics of metazoan
animals and failure
kinetics of manufactured
products. AGE, 1979, 2:
74-76.
Non-Gompertzian Mortality Kinetics
of Three Industrial Materials
Non-Gompertzian
mortality kinetics of three
industrial materials: steel,
industrial relays and
motor heat insulators.
Source: A. Economos.
A non-Gompertzian
paradigm for mortality
kinetics of metazoan
animals and failure
kinetics of manufactured
products. AGE, 1979, 2:
74-76.
Redundancy Creates Both Damage Tolerance
and Damage Accumulation (Aging)
Damage
Defect
No redundancy
Death
Damage
Defect
Redundancy
Damage accumulation
(aging)
Differences in reliability structure between
(a) technical devices and (b) biological systems
Statement of the HIDL hypothesis:
(Idea of High Initial Damage Load )
"Adult organisms already have an
exceptionally high load of initial damage,
which is comparable with the amount of
subsequent aging-related deterioration,
accumulated during the rest of the entire
adult life."
Source: Gavrilov, L.A. & Gavrilova, N.S. 1991. The Biology of Life Span:
A Quantitative Approach. Harwood Academic Publisher, New York.
Why should we expect high initial
damage load ?
• General argument:
-- In contrast to technical devices, which are built from pretested high-quality components, biological systems are formed by
self-assembly without helpful external quality control.
• Specific arguments:
1. Cell cycle checkpoints are disabled in early
development (Handyside, Delhanty,1997. Trends
Genet. 13, 270-275 )
2. extensive copy-errors in DNA, because most cell
divisions responsible for DNA copy-errors occur in
early-life (loss of telomeres is also particularly high in
early-life)
3. ischemia-reperfusion injury and asphyxia-reventilation
injury during traumatic process of 'normal' birth
Spontaneous mutant frequencies with
age in heart and small intestine
Small Intestine
Heart
35
-5
Mutant frequency (x10 )
40
30
25
20
15
10
5
0
0
5
10
15
20
Age (months)
25
30
35
Source: Presentation of Jan Vijg at the IABG Congress, Cambridge, 2003
Birth Process is a Potential
Source of High Initial Damage
•
During birth, the future child is deprived
of oxygen by compression of the
umbilical cord and suffers severe
hypoxia and asphyxia. Then, just after
birth, a newborn child is exposed to
oxidative stress because of acute
reoxygenation while starting to breathe.
It is known that acute reoxygenation
after hypoxia may produce extensive
oxidative damage through the same
mechanisms that produce ischemiareperfusion injury and the related
phenomenon, asphyxia-reventilation
injury. Asphyxia is a common
occurrence in the perinatal period, and
asphyxial brain injury is the most
common neurologic abnormality in the
neonatal period that may manifest in
neurologic disorders in later life.
Practical implications from
the HIDL hypothesis:
"Even a small progress in optimizing the early-developmental
processes can potentially result in a remarkable prevention of
many diseases in later life, postponement of aging-related
morbidity and mortality, and significant extension of healthy
lifespan."
"Thus, the idea of early-life programming of aging and longevity
may have important practical implications for developing earlylife interventions promoting health and longevity."
Source: Gavrilov, L.A. & Gavrilova, N.S. 1991. The Biology of Life Span:
A Quantitative Approach. Harwood Academic Publisher, New York.
Failure Kinetics in Mixtures of Systems with
Different Redundancy Levels
Initial Period
The dependence of
logarithm of
mortality force
(failure rate) as a
function of age in
mixtures of parallel
redundant systems
having Poisson
distribution by
initial numbers of
functional elements
(mean number of
elements,  = 1, 5,
10, 15, and 20.
Conclusions (I)
•
Redundancy is a key notion for understanding
aging and the systemic nature of aging in
particular. Systems, which are redundant in
numbers of irreplaceable elements, do deteriorate
(i.e., age) over time, even if they are built of nonaging elements.
•
An actuarial aging rate or expression of aging
(measured as age differences in failure rates,
including death rates) is higher for systems with
higher redundancy levels.
Conclusions (II)
•
Redundancy exhaustion over the life course explains the
observed ‘compensation law of mortality’ (mortality
convergence at later life) as well as the observed late-life
mortality deceleration, leveling-off, and mortality plateaus.
•
Living organisms seem to be formed with a high load of
initial damage, and therefore their lifespans and aging
patterns may be sensitive to early-life conditions that
determine this initial damage load during early
development. The idea of early-life programming of aging
and longevity may have important practical implications
for developing early-life interventions promoting health
and longevity.
Acknowledgments
This study was made possible thanks to:
• generous support from the National
Institute on Aging, and
• stimulating working environment at the
Center on Aging, NORC/University of
Chicago